eugeneyan/applied-ml GitHub repository
A curated collection of papers and articles from companies sharing real-world data science and machine learning applications in production.
What is eugeneyan/applied-ml GitHub repository?
Applied ML is a curated GitHub repository that aggregates papers, articles, and blog posts from major tech companies about their real-world applications of data science and machine learning in production. It helps practitioners understand how ML projects are implemented at scale, covering problem framing, technique selection, scientific rationale, and business outcomes. The collection addresses the gap between academic theory and industrial practice by providing concrete examples.
Data scientists, ML engineers, researchers, and technical leaders who need to learn from documented industry experiences to design, implement, and scale their own ML systems. It's particularly valuable for those transitioning models from research to production.
It offers a centralized, organized, and vetted source of practical ML knowledge from top companies, saving practitioners time searching for quality case studies. The focus on production details—including failures and ROI—provides insights often missing from academic papers or generic tutorials.
Overview
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
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